The background description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent the work is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.
3D computed tomography (CT) is the de-facto imaging standard for assessment of craniofacial malformation. Previous clinical studies have attempted to quantitatively analyze the different deformities of the skull. The studies rely on traditional anthropometric indices that are derived from linear measurements. However, such studies fail to provide a satisfactory representation of 3D skull morphology.
Shape analysis is another technique for assessing craniofacial malformations. For example, the use of point characterization along two dimensional profiles and the use of 3D point descriptions of the skull surface have been popularized. Such approaches require the surfaces of the skulls under study to be either sampled in accordance to a common geometric parameterization or sampled independently and subsequently aligned. The average skull morphology is then computed from the samples. Note that since the main goal of such works is the construction of a surgical template, the shape model is adjusted to only fit a set of linear measurements obtained from the subject.
A limitation of the above stated methodologies is the imposition of a “best-fit” or alignment dimensionality of a patient's CT scans only to those within the age group of the patient. Such techniques tend to be restrictive in that they ignore the possibility that one subject might be better represented by another age group after scale correction. It is desirable to have a computing platform that performs craniofacial diagnosis, severity assessment and surgical planning. Specifically, it is desirable that the process of diagnosis through surgical planning be automatic from start to finish. In other words, from the input of a patient's 3D CT data, the fusion status of the sutures, the severity of deformation, the true nearest normal solution, and the precise surgical intervention to achieve such, should readily follow.
Moreover another disadvantage in the prior shape analysis techniques is the means by which alignment metrics are determined. Specifically, the prior techniques employ either distance minimization between simplified segments of the skull surface or multiple user selected landmarks in order to define alignment, respectively. The former incorporates the malformed region into the alignment criteria which may be deficient for asymmetric malformations while the latter can result in a generally poor description of the anatomical requirements for alignment.
The definition of normal anatomy is another distinguishing feature between the prior methods. Some approaches use a normal shape model that is age and sex specific, comprised of twenty two groups. The model of normality is defined as the average distance from the dorsum-sella and its standard deviation at each point. However, such approaches assume that the shape variation occurs according to a fixed parameterization, an overly simplistic assumption. In other approaches, a tailored, artificial normal shape is defined that is adapted to the anatomy of the patient, using a statistical shape model derived from only twenty one scans. This shape model is tailored to a finite set of linear measurements that do not represent the full 3D geometry of the patient.
In addition to formalizing a definition of normal anatomy, determination of shape features (local characterization of shape on the skull surface) is also desired. Previous works compute abnormality using a statistical deviation from the average model at each point. However this method does not incorporate curvature features, which are particularly useful for metopic diagnosis. Such methods also rely heavily on arguable point correspondence and lack distance measurements (e.g., millimeters) that are required for surgical planning.
Furthermore, prior methods rely exclusively on age-appropriate average shape to evaluate deviations from “normal”, regardless of whether these models offer the best remodeling option for the surgeon. Accurate assessment or diagnosis of the degree of deformity in craniofacial disorders is a common goal of many prior works. However, previous works arbitrarily define a set of triangles on the surface of the skull and use these geometric relationships for diagnosis. Since these techniques are based neither upon shape analysis or modeling of abnormal anatomy, the results are suboptimal. Note that, if models of abnormality are available, the abnormality models may offer distinct advantages in understanding anatomical deformation in specific clinical conditions such as craniosynostosis which could lead to better assessment protocols.
As a complete and accurate assessment of dysmorphic and normal shape is a requisite for anatomy-normalizing surgical interventions, visualization schemes that illustrate the volumetric deviation of patient to “normal” play a significant role in assisting the surgeon. However, prior efforts in craniosynostosis techniques produce a visualization that do not represent physical measurements, but statistical ones, and also do not delineate the bones and sutures. This limits the application of previous teachings in applicability for surgical planning.